ual: camberwell chelsea wimbledon Unit Assessment Brief BSc (Hons) Creative Computing Unit Title: Creative Making: Art and Artificial Intelligence Unit Leader: Programme Administration Contact: Unit code: IU000120 Unit credit: 20 credits Unit duration: Year / Level: 3/6 Unit briefing date] Unit introduction Please read the Learning Materials that accompany this document. This may include project briefs, unit guidelines, glossary, additional reading lists or event and presentation information. This information will be published together on Moodle. In this unit we explore the application of machine learning techniques to creative practice and consider the cultural history of machine creativity. Creative practice based on ideas of artificial intelligence is not new but recent developments in computing power and machine leaning approaches have seen renewed interest in creative practice that both addresses and uses artificial intelligence. You will use computational approaches that leverage machine learning techniques to produce creative practice and explore the cultural idea of Al in a broad context. This practice may leverage current sound and image processing techniques, but you are also encouraged to explore how machine learning approaches may be integrated into creative practice more generally. This unit will also help you to situate Al arts practice in the context of the discourse of contemporary art and media arts practice. Learning outcomes and assessment criteria ual: camberwell chelsea wimbledon On completion of this unit, you will be able to LO1, Produce complex creative Al arts practice (Realisation) LO2, Use common approaches to Al arts practice (Knowledge) LO3, Recognise critical issues in Al arts practice (Enquiry) Assessment Criteria Your work in this unit will be marked against the UAL assessment criteria, which are designed to give you clear feedback on your achievement. The full assessment criteria descriptions can be found on the UAL Assessment webpage. ual: Assessment Criteria | Level 6 F E D с B A Enquiry ?? Engagement in practice informed Little or no by critical analysis and evaluation evidence Insufficient evidence Satisfactory evidence Good evidence Very good Excellent evidence evidence of diverse, complex concepts and ideas Knowledge Critical analysis of a range of practical, theoretical and/or Little or no evidence Insufficient evidence Satisfactory evidence Good evidence Very good Excellent evidence evidence technical knowledge(s) Process Experiment and evaluate methods, results and their Little or no evidence Insufficient evidence Satisfactory evidence Good evidence Very good Excellent evidence evidence implications Communication Demonstrating clarity and depth. Synthesis of diverse intentions, Little or no evidence Insufficient evidence Satisfactory evidence Good evidence Very good Excellent evidence evidence contexts and arguments appropriate to your audiences Realisation Meeting appropriate standards of professional production Little or no evidence Insufficient evidence Satisfactory evidence Good evidence Very good evidence Excellent evidence What you have to produce Holistic - This unit is assessed holistically (100% of the unit). Assessment will be against the specified marking criteria Assessment Description, Portfolio of work: documenting creative outcomes. This will also include a research weblog and/or sketchbook documenting iterative design and development process specifically. (100% Holistic) 2 ual: camberwell chelsea wimbledon Submission information Submission date and time: Holistic assessment (Portfolio of work): By Tuesday 30 January 2024 6:00pm (18:00) GMT Adjusted assessment submission date and time: By Tuesday 13 February 2024 6:00pm (18:00) GMT Adjusted Assessment (AA) is applicable to students with Individual Support Agreements. To confirm that you intend to use adjusted assessment, please email standard deadline. two weeks in advance of the Submission method: Anonymous marking: Zip folder via Moodle: A Zip folder (100MB max) containing a link to your GIT code repository and Readme PDF. The repository must include your portfolio work, documentation, research weblog and/or sketchbook outlining your iterative design and development process, and a Readme file which provides a description and comments on your work, exported as a PDF and submitted in the Zip No: It is not possible for this unit to be marked anonymously. However, all unit assessments are internally moderated to maintain fairness in assessment. (delete as appropriate) Date to expect feedback by: 20/02/2024 You will receive feedback online via Assessment Feedback. Please note grades and feedback are indicative until confirmed following the Exam Board. Submission queries: Please contact the submission deadline. Further information 3 in advance of ual: camberwell chelsea wimbledon • Completion of 3 small projects and reflections: Please complete 3 small projects using what you learned in class. Examples will be developed in class, and you could build from there or start something completely new. Please always be mindful of plagiarism. Your portfolio will be presented with documentation. • • • Students must complete 3 projects. These projects must constitute 2 'Creative projects' and 1 ‘Critical response' (examples below) The 'Critical response' should be between 500 and 1000 words and include citations (not included in word count), though does not have to be written in an academic 'tone' Students can apply skills and tools learnt in other units in this assignment Examples of projects could be but not limited to: 1. Use of AI/ML tools to produce an audio, visual or written piece such as work featuring generative AI/ML creation using a data set. (Creative project) 2. Use of government or other open-source data repositories to produce visual artwork or tell a story using AI/ML tools such as Runway ML. (Creative project) 3. Pick a societal issue and create a narrative based creative artifact (story, comic, design fiction) which explore how AI/ML could impact on this societal issue or raise awareness of it. (Creative project) 4. Pick a story or piece of media (literature, film, even computer game) that uses Al or robotics as a narrative device. Produce a critical/creative response such as a video essay, blog post, podcast that explores the role played by AI/ML in the narrative and how it reflects the potential of Al as it exists in the world. (Critical response). 5. Pick an existing example of a work, service, project, platform, or tool within the creative industries and re-imagine how an AI/ML tool could supplement or replace a human agent in this context. Produce a critical/creative response such as a video essay, blog post, podcast that explores the impact of the use of Al in this design/industrial context. (Critical response). Documentation: each of your creative projects will be presented with a 500-word documentation (or single essay or 1000 words) covering your iterative design approach, development process, ethical issues you might have considered, research and personal reflection. As usual, if you would like to provide different documentation (video, podcast, recorded presentation) this can be agreed upon with your tutor and as usual, we remain open to a creative way to produce documentation. If you provide a PDF and want to link to videos, please use QR codes (not URL links). Reading and resource list Essential Reading 4 ual: camberwell chelsea wimbledon Boden, M.A. (1998) 'Creativity and artificial intelligence', Artificial Intelligence, 103(1), pp. 347-356. Broeckmann, A. (2016) Machine Art in the Twentieth Century. MIT Press. Dewey, J. (2005) Art as Experience. Penguin. Kaplan, J. (2016) Artificial Intelligence: What Everyone Needs to Know. Oxford University Press. Kodratoff, Y. (2014) Introduction to Machine Learning. Elsevier. McCormack, J. and d'Inverno, M. (2012) Computers and Creativity. Springer Science & Business Media. Further Reading Aztiria, A., Augusto, J.C. and Orlandini, A. (2017) State of the Art in Al Applied to Ambient Intelligence. IOS Press. Bentley, P.J. and Corne, D.W. (2002) Creative Evolutionary Systems. Morgan Kaufmann. Géron, A. (2017) Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media, Inc. Millington, I. and Funge, J. (2016) Artificial Intelligence for Games. CRC Press. Pattanayak, S. (2017) Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial Intelligence in Python. Apress. Periodicals Artificial Intelligence Web Ref: https://www.creativeapplications.net Support CCW and UAL has a range of services that can support you with your studies. Useful contacts for advice and guidance include: CCW Academic Support Contact: 5